1 Results

1.1 1-d Heatmap

1.2 Location meda_plots

1.3 Outliers as given by randomForest

1.4 Correlation Matrix

1.5 Cumulative Variance with Elbows

1.6 Paired Hex-binned plot

1.7 Hierarchical GMM Classifications

1.8 Hierarchical GMM Dendrogram

1.9 Stacked Means

1.10 Cluster Means

2 Restricting hGMM to \(K = 2\)

Here we are restricting hierarchical GMM to only go through on level. We are comparing the cluster results to the glut labels.

set.seed(314)
h2 <- hmc(sdat, maxDepth = 2, ccol = ccol, maxDim = 12)
h2lab <- viridis(max(h2$dat$labels$col))
stackM(h2, ccol = ccol, centered = TRUE, depth = 1)

pairs(h2$dat$data, pch = 19, cex = 0.2, col = c("blue", "red")[gabaID$gaba+1])

p0 <- mclust::adjustedRandIndex(pred, gaba)
perms <- foreach(i = 1:1.5e4, .combine = c) %dopar% {
  set.seed(i*2)
  mclust::adjustedRandIndex(sample(pred), gaba)
}
t1
##      pred
## truth   0   1
##     0 647  61
##     1  45  40
hist(perms, xlim = c(min(perms), p0 + 0.25*p0),
     main = "permutation test of ARI values", probability = TRUE)
#hist(perms, probability = TRUE)
abline(v = p0, col = 'red')

3 Summary Table

measurment value
Misclassification Rate 0.1336696
Accuracy 0.8663304
Sensitivity 0.9138418
Specificity 0.4705882
Precision 0.9349711
Recall 0.9138418
F1-score 0.9242857
ARI 0.2949339